Bias detection and mitigation in AutoAI

This notebook contains the steps and code to demonstrate support of AutoAI experiments with bias detection/mitigation in Watson Machine Learning service. It introduces commands for data retrieval, training experiments, persisting pipelines, testing pipelines and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.9.

Learning goals

The learning goals of this notebook are:

• Work with Watson Machine Learning experiment to train AutoAI models with bias detection and mitigation.
• Compare trained models quality and fairness.

Contents

This notebook contains the following parts:

1. Setup
2. Optimizer definition
3. Bias detection and mitigation
4. Inspection of pipelines
5. Cleanup
6. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Create a Watson Machine Learning (WML) Service instance (a free plan is offered and information about how to create the instance can be found here).

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud. You need to provide platform api_key and instance location.

You can use IBM Cloud CLI to retrieve platform API Key and instance location.

API Key can be generated in the following way:

ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME

In result, get the value of api_key from the output.

Location of your WML instance can be retrieved in the following way:

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance WML_INSTANCE_NAME

In result, get the value of location from the output.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the Watson Machine Learning docs. You can check your instance location in your Watson Machine Learning (WML) Service instance details.

You can also get service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, then copy the created key and paste it below.

Action: Enter your api_key and location in the following cell.

api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

wml_credentials = {
    "apikey": api_key,
    "url": 'https://' + location + '.ml.cloud.ibm.com'
}

Install and import the ibm-watson-machine-learning and dependencies

Note: ibm-watson-machine-learning documentation can be found here.

!pip install -U ibm-watson-machine-learning | tail -n 1
!pip install -U autoai-libs | tail -n 1
!pip install scikit-learn==1.0.2 | tail -n 1
!pip install wget | tail -n 1
!pip install -U 'lale[fairness]' | tail -n 1

Working with spaces

First of all, you need to create a space that will be used for your work with AutoAI. If you do not have space already created, you can use Deployment Spaces Dashboard to create one.

• Click New Deployment Space
• Create an empty space
• Select Cloud Object Storage
• Select Watson Machine Learning instance and press Create
• Copy space_id and paste it below

Action: assign space ID below

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

client.spaces.list(limit=10)

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_credentials)
client.set.default_space(space_id)

'SUCCESS'

Optimizer definition

Training data connection

Define connection information to COS bucket and training data CSV file. This example uses the German Credit Risk dataset.

The dataset can be downloaded from here.

Action: Upload training data to COS bucket and enter location information below.

cos_credentials = client.spaces.get_details(space_id=space_id)['entity']['storage']['properties']

filename = 'german_credit_data_biased_training.csv'
datasource_name = 'bluemixcloudobjectstorage'
bucketname = cos_credentials['bucket_name']

Download training data from git repository.

import wget
import os

url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/bias/german_credit_data_biased_training.csv"
if not os.path.isfile(filename): 
    wget.download(url)

Create connection

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {datasource_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(datasource_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucketname,
        'access_key': cos_credentials['credentials']['editor']['access_key_id'],
        'secret_key': cos_credentials['credentials']['editor']['secret_access_key'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': cos_credentials['endpoint_url']
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

Creating connections...
SUCCESS

Note: The above connection can be initialized alternatively with api_key and resource_instance_id.
The above cell can be replaced with:

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(db_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucket_name,
        'api_key': cos_credentials['apikey'],
        'resource_instance_id': cos_credentials['resource_instance_id'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': 'https://s3.us.cloud-object-storage.appdomain.cloud'
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

Upload training data

from ibm_watson_machine_learning.helpers import DataConnection, S3Location

connection_id = client.connections.get_uid(conn_details)

credit_risk_conn = DataConnection(
    connection_asset_id=connection_id,
    location=S3Location(bucket=bucketname,
                        path=filename))

credit_risk_conn.set_client(client)
training_data_reference=[credit_risk_conn]
credit_risk_conn.write(data=filename, remote_name=filename)

Bias detection and mitigation

Terms and definitions:

Fairness Attribute - Bias or fairness is typically measured using some fairness attribute such as Gender, Ethnicity, Age, etc.

Monitored/Reference Group - Monitored group are those values of fairness attribute for which we want to measure bias. The rest of the values of the fairness attributes are called as reference group. In case of Fairness Attribute=Gender, if we are trying to measure bias against females, then Monitored group is “Female” and Reference group is “Male”.

Favourable/Unfavourable outcome - An important concept in bias detection is that of favourable and unfavourable outcome of the model. E.g., Claim approved can be considered as a favourable outcome and Claim denied can be considered as an unfavourable outcome.

Disparate Impact - metric used to measure bias (computed as the ratio of percentage of favourable outcome for the monitored group to the percentage of favourable outcome for the reference group). Bias is said to exist if the disparate impact value is below some threshold.

Optimizer configuration

Provide input information for AutoAI optimizer:

• name - experiment name
• prediction_type - type of the problem
• prediction_column - target column name
• fairness_info - bias detection configuration
• scoring - accuracy_and_disparate_impact combined optimization metric for both accuracy and fairness. For regression learning problem the r2_and_disparate_impact metric is supported (combines r2 and fairness).

fairness_info definition:

• protected_attributes (list of dicts) – subset of features for which fairness calculation is desired.

  • feature - name of feature for which reference_group and monitored_group are specified.
  • reference_group and monitored_group - monitored group are those values of fairness attribute for which we want to measure bias. The rest of the values of the fairness attribute are reference group.

• favorable_labels and unfavorable_labels – label values which are considered favorable (i.e. “positive”). unfavorable_labels are required when prediction type is regression.

Examples of supported configuration:

fairness_info = {
            "protected_attributes": [
                {"feature": "Age", "reference_group": [[26, 26], [30, 75]], 
                                    "monitored_group": [[18, 25], [27, 29]]}
            ],
            "favorable_labels": ["No Risk"]
            }

fairness_info = {
            "protected_attributes": [
                {"feature": "sex", "reference_group": ['male', 'not specified'], 
                                   "monitored_group": ['female']},
                {"feature": "age", "reference_group": [[26, 100]], "monitored_group": [[18, 25], [27, 29]]}
            ],
            "favorable_labels": [[5000.01, 9000]],
            "unfavorable_labels": [[0, 5000], [9000, 1000000]]
            }

fairness_info = {
            "protected_attributes": [
                {"feature": "Sex", "reference_group": ['male'], "monitored_group": ['female']},
                {"feature": "Age", "reference_group": [[26, 75]], "monitored_group": [[18, 25]]}
            ],
            "favorable_labels": ["No Risk"],
            "unfavorable_labels": ["Risk"],
}

from ibm_watson_machine_learning.experiment import AutoAI


experiment = AutoAI(wml_credentials, space_id=space_id)

pipeline_optimizer = experiment.optimizer(
    name='Credit Risk Prediction and bias detection - AutoAI',
    prediction_type=AutoAI.PredictionType.BINARY,
    prediction_column='Risk',
    scoring='accuracy_and_disparate_impact',
    fairness_info=fairness_info,
    max_number_of_estimators = 1,
    retrain_on_holdout=False
   )

Experiment run

Call the fit() method to trigger the AutoAI experiment. You can either use interactive mode (synchronous job) or background mode (asychronous job) by specifying background_model=True.

run_details = pipeline_optimizer.fit(
            training_data_reference=training_data_reference,
            background_mode=False)

Training job 4f4f9870-246a-4d4d-ab05-e1eddd40af00 completed: 100%|████████| [05:31<00:00,  3.31s/it]

You can use the get_run_status() method to monitor AutoAI jobs in background mode.

Get selected pipeline model

Download and reconstruct a scikit-learn pipeline model object from the AutoAI training job.

experiment_summary = pipeline_optimizer.summary()
experiment_summary.head()

	Enhancements	Estimator	training_disparate_impact_Sex	training_disparate_impact	holdout_accuracy_and_disparate_impact	training_roc_auc	holdout_disparate_impact_Sex	training_average_precision	training_accuracy_and_disparate_impact_(optimized)	training_log_loss	...	holdout_balanced_accuracy	training_recall	holdout_log_loss	training_accuracy	holdout_disparate_impact	holdout_roc_auc	training_balanced_accuracy	holdout_disparate_impact_Age	training_f1	training_disparate_impact_Age
Pipeline Name
Pipeline_3	HPO, FE	SnapDecisionTreeClassifier	1.118238	2.081132	0.148464	0.683937	1.171647	0.476717	0.258060	9.763075	...	0.702925	0.582514	9.413493	0.717333	1.653118	0.641539	0.683937	1.533538	0.580104	2.036895
Pipeline_4	HPO, FE	SnapDecisionTreeClassifier	1.118238	2.081132	0.148464	0.683937	1.171647	0.476717	0.258060	9.763075	...	0.702925	0.582514	9.413493	0.717333	1.653118	0.641539	0.683937	1.533538	0.580104	2.036895
Pipeline_1		SnapDecisionTreeClassifier	1.163020	1.946575	0.198434	0.691364	1.045489	0.484200	0.130193	9.601894	...	0.678937	0.598363	10.105659	0.722000	1.526765	0.684961	0.691364	1.472074	0.590830	2.038237
Pipeline_2	HPO	SnapDecisionTreeClassifier	1.163020	1.946575	0.198434	0.691364	1.045489	0.484200	0.130193	9.601894	...	0.678937	0.598363	10.105659	0.722000	1.526765	0.684961	0.691364	1.472074	0.590830	2.038237

4 rows × 22 columns

Visualize pipeline

pipeline_name = experiment_summary.index[experiment_summary.holdout_disparate_impact.argmax()]
best_pipeline = pipeline_optimizer.get_pipeline(pipeline_name=pipeline_name)
best_pipeline.visualize()

Each node in the visualization is a machine-learning operator (transformer or estimator). Each edge indicates data flow (transformed output from one operator becomes input to the next). The input to the root nodes is the initial dataset and the output from the sink node is the final prediction. When you hover the mouse pointer over a node, a tooltip shows you the configuration arguments of the corresponding operator (tuned hyperparameters). When you click on the hyperlink of a node, it brings you to a documentation page for the operator.

Test pipeline model locally

Read the data

X_train, X_holdout, y_train, y_holdout = pipeline_optimizer.get_data_connections()[0].read(with_holdout_split=True)

Calculate metrics

For detail description of used metrics you can check the documentation:

• accuracy

• disparate_impact

• accuracy and disparate impact

from lale.lib.aif360 import disparate_impact, accuracy_and_disparate_impact
from sklearn.metrics import accuracy_score

predicted_y = best_pipeline.predict(X_holdout.values)
disparate_impact_scorer = disparate_impact(**fairness_info)
accuracy_disparate_impact_scorer = accuracy_and_disparate_impact(**fairness_info)

print("Accuracy: {:.2f}".format(accuracy_score(y_true= y_holdout, y_pred=predicted_y)))
print("Disparate impact: {:.2f}".format(disparate_impact_scorer(best_pipeline, X_holdout, y_holdout)))
print("Accuracy and disparate impact: {:.2f}".format(accuracy_disparate_impact_scorer(best_pipeline, X_holdout, y_holdout)))

Accuracy: 0.73
Disparate impact: 1.65
Accuracy and disparate impact: 0.15

---

Fairness insights

You can analize favorable outcome distributions using visualize method from utils module.

from ibm_watson_machine_learning.utils.autoai.fairness import visualize

visualize(run_details, pipeline_name)

---

Clean up

If you want to clean up all created assets:

• experiments
• trainings
• pipelines
• model definitions
• models
• functions
• deployments

please follow up this sample notebook.

Summary and next steps

You successfully completed this notebook!

As a next step you can deploy and score the model: Sample notebook.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Lukasz Cmielowski, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Dorota Lączak, software engineer in Watson Machine Learning at IBM

Szymon Kucharczyk, software engineer in Watson Machine Learning at IBM

Copyright © 2021 IBM. This notebook and its source code are released under the terms of the MIT License.